/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    Normalize.java
 *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.filters.unsupervised.attribute;

import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.SparseInstance;
import weka.core.Utils;
import weka.core.Capabilities.Capability;
import weka.filters.Sourcable;
import weka.filters.UnsupervisedFilter;

import java.util.Enumeration;
import java.util.Vector;

/**
 * <!-- globalinfo-start --> Normalizes all numeric values in the given dataset
 * (apart from the class attribute, if set). The resulting values are by default
 * in [0,1] for the data used to compute the normalization intervals. But with
 * the scale and translation parameters one can change that, e.g., with scale =
 * 2.0 and translation = -1.0 you get values in the range [-1,+1].
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 * -unset-class-temporarily
 *  Unsets the class index temporarily before the filter is
 *  applied to the data.
 *  (default: no)
 * </pre>
 * 
 * <pre>
 * -S &lt;num&gt;
 *  The scaling factor for the output range.
 *  (default: 1.0)
 * </pre>
 * 
 * <pre>
 * -T &lt;num&gt;
 *  The translation of the output range.
 *  (default: 0.0)
 * </pre>
 * 
 * <!-- options-end -->
 * 
 * @author Eibe Frank (eibe@cs.waikato.ac.nz)
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision: 5543 $
 */
public class Normalize extends PotentialClassIgnorer implements
		UnsupervisedFilter, Sourcable, OptionHandler {

	/** for serialization. */
	static final long serialVersionUID = -8158531150984362898L;

	/** The minimum values for numeric attributes. */
	protected double[] m_MinArray;

	/** The maximum values for numeric attributes. */
	protected double[] m_MaxArray;

	/** The translation of the output range. */
	protected double m_Translation = 0;

	/** The scaling factor of the output range. */
	protected double m_Scale = 1.0;

	/**
	 * Returns a string describing this filter.
	 * 
	 * @return a description of the filter suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String globalInfo() {
		return "Normalizes all numeric values in the given dataset (apart from the "
				+ "class attribute, if set). The resulting values are by default "
				+ "in [0,1] for the data used to compute the normalization intervals. "
				+ "But with the scale and translation parameters one can change that, "
				+ "e.g., with scale = 2.0 and translation = -1.0 you get values in the "
				+ "range [-1,+1].";
	}

	/**
	 * Returns an enumeration describing the available options.
	 * 
	 * @return an enumeration of all the available options.
	 */
	public Enumeration listOptions() {
		Vector result = new Vector();

		Enumeration en = super.listOptions();
		while (en.hasMoreElements())
			result.addElement(en.nextElement());

		result.addElement(new Option(
				"\tThe scaling factor for the output range.\n"
						+ "\t(default: 1.0)", "S", 1, "-S <num>"));

		result.addElement(new Option("\tThe translation of the output range.\n"
				+ "\t(default: 0.0)", "T", 1, "-T <num>"));

		return result.elements();
	}

	/**
	 * Parses a given list of options.
	 * <p/>
	 * 
	 * <!-- options-start --> Valid options are:
	 * <p/>
	 * 
	 * <pre>
	 * -unset-class-temporarily
	 *  Unsets the class index temporarily before the filter is
	 *  applied to the data.
	 *  (default: no)
	 * </pre>
	 * 
	 * <pre>
	 * -S &lt;num&gt;
	 *  The scaling factor for the output range.
	 *  (default: 1.0)
	 * </pre>
	 * 
	 * <pre>
	 * -T &lt;num&gt;
	 *  The translation of the output range.
	 *  (default: 0.0)
	 * </pre>
	 * 
	 * <!-- options-end -->
	 * 
	 * @param options
	 *            the list of options as an array of strings
	 * @throws Exception
	 *             if an option is not supported
	 */
	public void setOptions(String[] options) throws Exception {
		String tmpStr;

		tmpStr = Utils.getOption('S', options);
		if (tmpStr.length() != 0)
			setScale(Double.parseDouble(tmpStr));
		else
			setScale(1.0);

		tmpStr = Utils.getOption('T', options);
		if (tmpStr.length() != 0)
			setTranslation(Double.parseDouble(tmpStr));
		else
			setTranslation(0.0);

		if (getInputFormat() != null)
			setInputFormat(getInputFormat());
	}

	/**
	 * Gets the current settings of the filter.
	 * 
	 * @return an array of strings suitable for passing to setOptions
	 */
	public String[] getOptions() {
		Vector<String> result;

		result = new Vector<String>();

		result.add("-S");
		result.add("" + getScale());

		result.add("-T");
		result.add("" + getTranslation());

		return result.toArray(new String[result.size()]);
	}

	/**
	 * Returns the Capabilities of this filter.
	 * 
	 * @return the capabilities of this object
	 * @see Capabilities
	 */
	public Capabilities getCapabilities() {
		Capabilities result = super.getCapabilities();
		result.disableAll();

		// attributes
		result.enableAllAttributes();
		result.enable(Capability.MISSING_VALUES);

		// class
		result.enableAllClasses();
		result.enable(Capability.MISSING_CLASS_VALUES);
		result.enable(Capability.NO_CLASS);

		return result;
	}

	/**
	 * Sets the format of the input instances.
	 * 
	 * @param instanceInfo
	 *            an Instances object containing the input instance structure
	 *            (any instances contained in the object are ignored - only the
	 *            structure is required).
	 * @return true if the outputFormat may be collected immediately
	 * @throws Exception
	 *             if the input format can't be set successfully
	 */
	public boolean setInputFormat(Instances instanceInfo) throws Exception {

		super.setInputFormat(instanceInfo);
		setOutputFormat(instanceInfo);
		m_MinArray = m_MaxArray = null;
		return true;
	}

	/**
	 * Input an instance for filtering. Filter requires all training instances
	 * be read before producing output.
	 * 
	 * @param instance
	 *            the input instance
	 * @return true if the filtered instance may now be collected with output().
	 * @throws Exception
	 *             if an error occurs
	 * @throws IllegalStateException
	 *             if no input format has been set.
	 */
	public boolean input(Instance instance) throws Exception {
		if (getInputFormat() == null)
			throw new IllegalStateException("No input instance format defined");

		if (m_NewBatch) {
			resetQueue();
			m_NewBatch = false;
		}
		if (m_MinArray == null) {
			bufferInput(instance);
			return false;
		} else {
			convertInstance(instance);
			return true;
		}
	}

	/**
	 * Signify that this batch of input to the filter is finished. If the filter
	 * requires all instances prior to filtering, output() may now be called to
	 * retrieve the filtered instances.
	 * 
	 * @return true if there are instances pending output
	 * @throws Exception
	 *             if an error occurs
	 * @throws IllegalStateException
	 *             if no input structure has been defined
	 */
	public boolean batchFinished() throws Exception {
		if (getInputFormat() == null)
			throw new IllegalStateException("No input instance format defined");

		if (m_MinArray == null) {
			Instances input = getInputFormat();
			// Compute minimums and maximums
			m_MinArray = new double[input.numAttributes()];
			m_MaxArray = new double[input.numAttributes()];
			for (int i = 0; i < input.numAttributes(); i++)
				m_MinArray[i] = Double.NaN;

			for (int j = 0; j < input.numInstances(); j++) {
				double[] value = input.instance(j).toDoubleArray();
				for (int i = 0; i < input.numAttributes(); i++) {
					if (input.attribute(i).isNumeric()
							&& (input.classIndex() != i)) {
						if (!Instance.isMissingValue(value[i])) {
							if (Double.isNaN(m_MinArray[i])) {
								m_MinArray[i] = m_MaxArray[i] = value[i];
							} else {
								if (value[i] < m_MinArray[i])
									m_MinArray[i] = value[i];
								if (value[i] > m_MaxArray[i])
									m_MaxArray[i] = value[i];
							}
						}
					}
				}
			}

			// Convert pending input instances
			for (int i = 0; i < input.numInstances(); i++)
				convertInstance(input.instance(i));
		}
		// Free memory
		flushInput();

		m_NewBatch = true;
		return (numPendingOutput() != 0);
	}

	/**
	 * Convert a single instance over. The converted instance is added to the
	 * end of the output queue.
	 * 
	 * @param instance
	 *            the instance to convert
	 * @throws Exception
	 *             if conversion fails
	 */
	protected void convertInstance(Instance instance) throws Exception {
		Instance inst = null;
		if (instance instanceof SparseInstance) {
			double[] newVals = new double[instance.numAttributes()];
			int[] newIndices = new int[instance.numAttributes()];
			double[] vals = instance.toDoubleArray();
			int ind = 0;
			for (int j = 0; j < instance.numAttributes(); j++) {
				double value;
				if (instance.attribute(j).isNumeric()
						&& (!Instance.isMissingValue(vals[j]))
						&& (getInputFormat().classIndex() != j)) {
					if (Double.isNaN(m_MinArray[j])
							|| (m_MaxArray[j] == m_MinArray[j])) {
						value = 0;
					} else {
						value = (vals[j] - m_MinArray[j])
								/ (m_MaxArray[j] - m_MinArray[j]) * m_Scale
								+ m_Translation;
						if (Double.isNaN(value)) {
							throw new Exception("A NaN value was generated "
									+ "while normalizing "
									+ instance.attribute(j).name());
						}
					}
					if (value != 0.0) {
						newVals[ind] = value;
						newIndices[ind] = j;
						ind++;
					}
				} else {
					value = vals[j];
					if (value != 0.0) {
						newVals[ind] = value;
						newIndices[ind] = j;
						ind++;
					}
				}
			}
			double[] tempVals = new double[ind];
			int[] tempInd = new int[ind];
			System.arraycopy(newVals, 0, tempVals, 0, ind);
			System.arraycopy(newIndices, 0, tempInd, 0, ind);
			inst = new SparseInstance(instance.weight(), tempVals, tempInd,
					instance.numAttributes());
		} else {
			double[] vals = instance.toDoubleArray();
			for (int j = 0; j < getInputFormat().numAttributes(); j++) {
				if (instance.attribute(j).isNumeric()
						&& (!Instance.isMissingValue(vals[j]))
						&& (getInputFormat().classIndex() != j)) {
					if (Double.isNaN(m_MinArray[j])
							|| (m_MaxArray[j] == m_MinArray[j])) {
						vals[j] = 0;
					} else {
						vals[j] = (vals[j] - m_MinArray[j])
								/ (m_MaxArray[j] - m_MinArray[j]) * m_Scale
								+ m_Translation;
						if (Double.isNaN(vals[j])) {
							throw new Exception("A NaN value was generated "
									+ "while normalizing "
									+ instance.attribute(j).name());
						}
					}
				}
			}
			inst = new Instance(instance.weight(), vals);
		}
		inst.setDataset(instance.dataset());
		push(inst);
	}

	/**
	 * Returns a string that describes the filter as source. The filter will be
	 * contained in a class with the given name (there may be auxiliary
	 * classes), and will contain two methods with these signatures:
	 * 
	 * <pre>
	 * <code>
	 * // converts one row
	 * public static Object[] filter(Object[] i);
	 * // converts a full dataset (first dimension is row index)
	 * public static Object[][] filter(Object[][] i);
	 * </code>
	 * </pre>
	 * 
	 * where the array <code>i</code> contains elements that are either Double,
	 * String, with missing values represented as null. The generated code is
	 * public domain and comes with no warranty.
	 * 
	 * @param className
	 *            the name that should be given to the source class.
	 * @param data
	 *            the dataset used for initializing the filter
	 * @return the object source described by a string
	 * @throws Exception
	 *             if the source can't be computed
	 */
	public String toSource(String className, Instances data) throws Exception {
		StringBuffer result;
		boolean[] process;
		int i;

		result = new StringBuffer();

		// determine what attributes were processed
		process = new boolean[data.numAttributes()];
		for (i = 0; i < data.numAttributes(); i++)
			process[i] = (data.attribute(i).isNumeric() && (i != data
					.classIndex()));

		result.append("class " + className + " {\n");
		result.append("\n");
		result.append("  /** lists which attributes will be processed */\n");
		result.append("  protected final static boolean[] PROCESS = new boolean[]{"
				+ Utils.arrayToString(process) + "};\n");
		result.append("\n");
		result.append("  /** the minimum values for numeric values */\n");
		result.append("  protected final static double[] MIN = new double[]{"
				+ Utils.arrayToString(m_MinArray).replaceAll("NaN",
						"Double.NaN") + "};\n");
		result.append("\n");
		result.append("  /** the maximum values for numeric values */\n");
		result.append("  protected final static double[] MAX = new double[]{"
				+ Utils.arrayToString(m_MaxArray) + "};\n");
		result.append("\n");
		result.append("  /** the scale factor */\n");
		result.append("  protected final static double SCALE = " + m_Scale
				+ ";\n");
		result.append("\n");
		result.append("  /** the translation */\n");
		result.append("  protected final static double TRANSLATION = "
				+ m_Translation + ";\n");
		result.append("\n");
		result.append("  /**\n");
		result.append("   * filters a single row\n");
		result.append("   * \n");
		result.append("   * @param i the row to process\n");
		result.append("   * @return the processed row\n");
		result.append("   */\n");
		result.append("  public static Object[] filter(Object[] i) {\n");
		result.append("    Object[] result;\n");
		result.append("\n");
		result.append("    result = new Object[i.length];\n");
		result.append("    for (int n = 0; n < i.length; n++) {\n");
		result.append("      if (PROCESS[n] && (i[n] != null)) {\n");
		result.append("        if (Double.isNaN(MIN[n]) || (MIN[n] == MAX[n]))\n");
		result.append("          result[n] = 0;\n");
		result.append("        else\n");
		result.append("          result[n] = (((Double) i[n]) - MIN[n]) / (MAX[n] - MIN[n]) * SCALE + TRANSLATION;\n");
		result.append("      }\n");
		result.append("      else {\n");
		result.append("        result[n] = i[n];\n");
		result.append("      }\n");
		result.append("    }\n");
		result.append("\n");
		result.append("    return result;\n");
		result.append("  }\n");
		result.append("\n");
		result.append("  /**\n");
		result.append("   * filters multiple rows\n");
		result.append("   * \n");
		result.append("   * @param i the rows to process\n");
		result.append("   * @return the processed rows\n");
		result.append("   */\n");
		result.append("  public static Object[][] filter(Object[][] i) {\n");
		result.append("    Object[][] result;\n");
		result.append("\n");
		result.append("    result = new Object[i.length][];\n");
		result.append("    for (int n = 0; n < i.length; n++) {\n");
		result.append("      result[n] = filter(i[n]);\n");
		result.append("    }\n");
		result.append("\n");
		result.append("    return result;\n");
		result.append("  }\n");
		result.append("}\n");

		return result.toString();
	}

	/**
	 * Returns the calculated minimum values for the attributes in the data.
	 * 
	 * @return the array with the minimum values
	 */
	public double[] getMinArray() {
		return m_MinArray;
	}

	/**
	 * Returns the calculated maximum values for the attributes in the data.
	 * 
	 * @return the array with the maximum values
	 */
	public double[] getMaxArray() {
		return m_MaxArray;
	}

	/**
	 * Returns the tip text for this property.
	 * 
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String scaleTipText() {
		return "The factor for scaling the output range (default: 1).";
	}

	/**
	 * Get the scaling factor.
	 * 
	 * @return the factor
	 */
	public double getScale() {
		return m_Scale;
	}

	/**
	 * Sets the scaling factor.
	 * 
	 * @param value
	 *            the scaling factor
	 */
	public void setScale(double value) {
		m_Scale = value;
	}

	/**
	 * Returns the tip text for this property.
	 * 
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String translationTipText() {
		return "The translation of the output range (default: 0).";
	}

	/**
	 * Get the translation.
	 * 
	 * @return the translation
	 */
	public double getTranslation() {
		return m_Translation;
	}

	/**
	 * Sets the translation.
	 * 
	 * @param value
	 *            the translation
	 */
	public void setTranslation(double value) {
		m_Translation = value;
	}

	/**
	 * Returns the revision string.
	 * 
	 * @return the revision
	 */
	public String getRevision() {
		return RevisionUtils.extract("$Revision: 5543 $");
	}

	/**
	 * Main method for running this filter.
	 * 
	 * @param args
	 *            should contain arguments to the filter, use -h for help
	 */
	public static void main(String[] args) {
		runFilter(new Normalize(), args);
	}
}
